We aim to see the patterns of infant mortality among maternal populations based on their tobacco and alcohol consumption patterns. Additionally, we also want to see how demographic factors such as race, age, income levels, educational levels and intent to have children can also contribute to infant mortality rates.

Plots of Maternal Alcohol Consumption

# Plot of question and responses for alcohol

# Create ggplot object
gg_plot <- cleaned_alc_2007 %>%
  ggplot(aes(x = question, fill = response)) +
  geom_bar(position = "dodge") +
  labs(title = "Questions and Responses", x = "Questions", y = "Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 5, size = 2)) +
  labs(
    x = "Question",
    y = "Response",
    title = "Questions vs Response of Alcohol Consumption"
  )
# Extract data directly from the original data frame
plot_data <- cleaned_alc_2007 %>%
  group_by(question, response) %>%
  summarize(count = n())
## `summarise()` has grouped output by 'question'. You can override using
## the `.groups` argument.
# Convert data to Plotly
plot_ly(data = plot_data, x = ~question, y = ~count, color = ~response, type = "bar", split = ~response) %>%
  layout(
    title = "Questions vs Response of Alcohol Consumption",
    xaxis = list(title = "Question",tickfont = list(size = 5)),
    yaxis = list(title = "Response"),
    barmode = "stack"
  )
<<<<<<< HEAD
=======
>>>>>>> 61ad8b81651148b582c777369aef8cb1b06c3d0b

Plot 1 shows the number of individuals who consumed alcohol, resumed drinking alcohol after quitting briefly, or reduced the total number of drinks consumed. The x-axis shows the indicators from the CDC 2007 PRAM data set, and y-axis shows the data values. Overall, it looks like the number of mothers who had the same drinks or more had the highest amount of data values.

Plots of Maternal Tobacco Use

library(ggplot2)
library(plotly)
library(dplyr)


# Create ggplot object
gg_plot <- cleaned_tobac_2007 %>%
  ggplot(aes(x = location_abbr, fill = response)) +
  geom_bar(position = "dodge") +
  labs(title = "Questions and Responses", x = "Questions", y = "Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(
    x = "Question",
    y = "Response",
    title = "Tobacco Use by State"
  )

# Extract data directly from the original data frame
plot_data <- cleaned_tobac_2007 %>%
  group_by(location_abbr, response) %>%
  summarize(count = n())
<<<<<<< HEAD 
## `summarise()` has grouped output by 'location_abbr'. You
## can override using the `.groups` argument.
======= 
## `summarise()` has grouped output by 'location_abbr'. You can override
## using the `.groups` argument.
>>>>>>> 61ad8b81651148b582c777369aef8cb1b06c3d0b 
# Convert data to Plotly
plot_ly(data = plot_data, x = ~location_abbr, y = ~count, color = ~response, type = "bar", split = ~response) %>%
  layout(
    title = " Tobacco Use by State",
    xaxis = list(title = "Question"),
    yaxis = list(title = "Response"),
    barmode = "stack"
  )
<<<<<<< HEAD
=======
>>>>>>> 61ad8b81651148b582c777369aef8cb1b06c3d0b

Plot 2 shows tobacco consumption by state according to the 2007 CDC PRAM data set. The highest data values were amongst those who answered “yes” and the lowest data values were among those who were specific and answered “41+/ day.”

Map of Maternal Alcohol Use

leaflet() |> 
  addTiles() |> 
  addCircleMarkers(data = cleaned_alc_2007,
                   lng = ~longitude,  # Adjust column name if needed
                   lat = ~latitude,   # Adjust column name if needed
                   label = ~location_abbr,   # Assuming 'Group.1' is a column in your data
                   radius = ~data_value * 0.12,
                   color = "blue",
                   stroke = TRUE,
                   fillOpacity = 0.1,
                   popup = ~paste("Response:", response))
<<<<<<< HEAD
=======
>>>>>>> 61ad8b81651148b582c777369aef8cb1b06c3d0b

This map visualizes data points from the cleaned_tobac_2007 dataset, showcasing the prevalence of Tobacco Use during pregnancy across different states. The sizes of the circles represent the values in the data_value column, reflecting the number of participants who responded affirmatively to Tobacco Use during pregnancy in each state. Larger circles indicate a higher number of positive responses, offering an overview of the distribution of this behavior across geographic regions

Map of Maternal Tobacco use

leaflet() %>%
  addTiles() %>%
  addCircleMarkers(data = cleaned_tobac_2007,
                   lng = ~longitude,  
                   lat = ~latitude,   
                   label = ~location_abbr,   
                   radius = ~data_value * 0.12,
                   color = "magenta",
                   stroke = TRUE,
                   fillOpacity = 0.1,
                   popup = ~paste("Response:", response),
                   group = ~location_abbr)
<<<<<<< HEAD
=======
>>>>>>> 61ad8b81651148b582c777369aef8cb1b06c3d0b

This map illustrates data points from the cleaned_tobac_2007 dataset, depicting the prevalence of Tobacco Use during pregnancy across various states. The sizes of the circles correspond to the values in the data_value column, reflecting the number of participants who reported affirmative responses to Tobacco Use during pregnancy in each state. Larger circles indicate a higher number of positive responses, providing insight into the regional distribution of this behavior

Map of Infant Mortality Rate

leaflet() %>%
  addTiles() %>%
  addCircleMarkers(data = cleaned_infant_mortality,
                   lng = ~longitude,
                   lat = ~latitude,
                   label = ~location_abbr,
                   radius = ~data_value * 0.12,
                   color = "orange",
                   stroke = TRUE,
                   fillOpacity = 0.5,
                   popup = ~paste("Response:", response))
<<<<<<< HEAD
=======
>>>>>>> 61ad8b81651148b582c777369aef8cb1b06c3d0b

This Leaflet plot provides a visual representation of infant mortality data on a map. The size of the circles corresponds to the values in the data_value column, indicating the rate of infant mortality for each location.

Infant Mortality by State

The plot above shows the locations of infant mortality rate across the US.

infant_deaths <- cleaned_infant_mortality %>%
  filter(question == "Indicator of infant currently alive" & response == "NO") %>%
  group_by(location_desc) %>%
  summarize(total_infant_deaths = n())

# Display the table using knitr::kable()
knitr::kable(infant_deaths)
location_desc total_infant_deaths
Alaska 45
Arkansas 45
Colorado 47
Delaware 40
Georgia 43
Hawaii 45
Illinois 47
Maine 42
Maryland 45
Massachusetts 44
Michigan 43
Minnesota 41
Missouri 42
Nebraska 45
New Jersey 39
New York (excluding NYC) 47
New York City 47
North Carolina 47
Ohio 46
Oklahoma 47
Oregon 46
Pennsylvania 3
Rhode Island 46
South Carolina 47
South Dakota 43
Utah 47
Vermont 47
Washington 43
West Virginia 47
Wisconsin 40
Wyoming 43

The table provides a summary of total infant deaths by state, with each row representing a specific location. The location_desc column denotes the state, and the total_infant_deaths column indicates the corresponding number of infant deaths in each location. The data suggests variability in infant mortality rates across different regions, with some areas reporting higher or lower rates than others. For instance, states like Pennsylvania have a notably lower count of infant deaths, while others, such as Alaska and Arkansas, have higher counts. However, most of the data seemed to stay within the 35 to 50 range. This summary provides an overview of the distribution of infant deaths across various geographical locations.

Looking at infant mortality rates by race, income levels, age, education levels and intent.

Table showing Infant Mortality by Race/Ethnicity

filtered_mortality_race <- cleaned_infant_mortality %>%
  filter(break_out_category == "Maternal Race/Ethnicity" &
         (break_out %in% c("Hispanic", "Non-hispanic", "White, non-Hispanic")) &
         question == "Indicator of infant currently alive" & response == "NO")

# Display the table using knitr::kable()
knitr::kable(filtered_mortality_race)
year location_abbr location_desc class topic question data_source response data_value low_confidence_limit high_confidence_limit sample_size break_out break_out_category latitude longitude class_id topic_id question_id location_id break_out_id break_out_categoryid response_id
2007 UT Utah Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.9 0.3 2.7 5 Hispanic Maternal Race/Ethnicity 39.36070 -111.58713 CLA8 TOP43 QUO143 49 ETH2 BOC6 RES23
2007 OR Oregon Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.7 0.2 2.1 3 Hispanic Maternal Race/Ethnicity 44.56745 -120.15503 CLA8 TOP43 QUO143 41 ETH2 BOC6 RES23
2007 WA Washington Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.2 0.0 1.7 1 White, non-Hispanic Maternal Race/Ethnicity 47.52228 -120.47001 CLA8 TOP43 QUO143 53 ETH4 BOC6 RES23
2007 YC New York City Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.4 0.2 0.7 10 Hispanic Maternal Race/Ethnicity 42.82700 -75.54397 CLA8 TOP43 QUO143 36 ETH2 BOC6 RES23
2007 OH Ohio Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.6 0.3 1.4 18 White, non-Hispanic Maternal Race/Ethnicity 40.06021 -82.40426 CLA8 TOP43 QUO143 39 ETH4 BOC6 RES23
2007 ME Maine Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO NA NA NA NA Hispanic Maternal Race/Ethnicity 45.25423 -68.98503 CLA8 TOP43 QUO143 23 ETH2 BOC6 RES23
2007 MD Maryland Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.6 0.3 1.3 7 Hispanic Maternal Race/Ethnicity 39.29058 -76.60926 CLA8 TOP43 QUO143 24 ETH2 BOC6 RES23
2007 ME Maine Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.3 0.2 0.4 19 White, non-Hispanic Maternal Race/Ethnicity 45.25423 -68.98503 CLA8 TOP43 QUO143 23 ETH4 BOC6 RES23
2007 MA Massachusetts Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.6 0.1 2.2 2 Hispanic Maternal Race/Ethnicity 42.27687 -72.08269 CLA8 TOP43 QUO143 25 ETH2 BOC6 RES23
2007 IL Illinois Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.4 0.2 0.6 12 White, non-Hispanic Maternal Race/Ethnicity 40.48501 -88.99771 CLA8 TOP43 QUO143 17 ETH4 BOC6 RES23
2007 DE Delaware Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 1.8 0.6 5.4 4 Hispanic Maternal Race/Ethnicity 39.00883 -75.57774 CLA8 TOP43 QUO143 10 ETH2 BOC6 RES23
2007 MO Missouri Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.8 0.5 1.5 18 White, non-Hispanic Maternal Race/Ethnicity 38.63579 -92.56630 CLA8 TOP43 QUO143 29 ETH4 BOC6 RES23
2007 AR Arkansas Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.3 0.1 1.0 2 Hispanic Maternal Race/Ethnicity 34.74865 -92.27449 CLA8 TOP43 QUO143 5 ETH2 BOC6 RES23
2007 RI Rhode Island Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.3 0.2 0.6 5 Hispanic Maternal Race/Ethnicity 41.70828 -71.52247 CLA8 TOP43 QUO143 44 ETH2 BOC6 RES23
2007 WV West Virginia Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO NA NA NA NA Hispanic Maternal Race/Ethnicity 38.66551 -80.71264 CLA8 TOP43 QUO143 54 ETH2 BOC6 RES23
2007 UT Utah Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.5 0.2 1.0 15 White, non-Hispanic Maternal Race/Ethnicity 39.36070 -111.58713 CLA8 TOP43 QUO143 49 ETH4 BOC6 RES23
2007 MA Massachusetts Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.5 0.1 1.9 2 White, non-Hispanic Maternal Race/Ethnicity 42.27687 -72.08269 CLA8 TOP43 QUO143 25 ETH4 BOC6 RES23
2007 AR Arkansas Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.5 0.2 1.1 18 White, non-Hispanic Maternal Race/Ethnicity 34.74865 -92.27449 CLA8 TOP43 QUO143 5 ETH4 BOC6 RES23
2007 SD South Dakota Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO NA NA NA NA Hispanic Maternal Race/Ethnicity 44.35313 -100.37353 CLA8 TOP43 QUO143 46 ETH2 BOC6 RES23
2007 HI Hawaii Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.4 0.1 2.5 1 White, non-Hispanic Maternal Race/Ethnicity 21.30485 -157.85775 CLA8 TOP43 QUO143 15 ETH4 BOC6 RES23
2007 VT Vermont Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO NA NA NA NA Hispanic Maternal Race/Ethnicity 43.62538 -72.51764 CLA8 TOP43 QUO143 50 ETH2 BOC6 RES23
2007 MD Maryland Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.1 0.1 0.3 8 White, non-Hispanic Maternal Race/Ethnicity 39.29058 -76.60926 CLA8 TOP43 QUO143 24 ETH4 BOC6 RES23
2007 MN Minnesota Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 1.1 0.2 7.4 1 Hispanic Maternal Race/Ethnicity 46.35565 -94.79420 CLA8 TOP43 QUO143 27 ETH2 BOC6 RES23
2007 IL Illinois Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.6 0.2 1.9 4 Hispanic Maternal Race/Ethnicity 40.48501 -88.99771 CLA8 TOP43 QUO143 17 ETH2 BOC6 RES23
2007 WA Washington Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.2 0.0 1.3 1 Hispanic Maternal Race/Ethnicity 47.52228 -120.47001 CLA8 TOP43 QUO143 53 ETH2 BOC6 RES23
2007 NY New York (excluding NYC) Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.4 0.1 1.0 4 Hispanic Maternal Race/Ethnicity 42.82700 -75.54397 CLA8 TOP43 QUO143 36 ETH2 BOC6 RES23
2007 OR Oregon Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.6 0.2 1.7 18 White, non-Hispanic Maternal Race/Ethnicity 44.56745 -120.15503 CLA8 TOP43 QUO143 41 ETH4 BOC6 RES23
2007 NC North Carolina Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.7 0.3 1.3 9 Hispanic Maternal Race/Ethnicity 35.46622 -79.15925 CLA8 TOP43 QUO143 37 ETH2 BOC6 RES23
2007 NJ New Jersey Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.2 0.1 0.7 3 White, non-Hispanic Maternal Race/Ethnicity 40.13057 -74.27369 CLA8 TOP43 QUO143 34 ETH4 BOC6 RES23
2007 NY New York (excluding NYC) Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.6 0.3 1.4 19 White, non-Hispanic Maternal Race/Ethnicity 42.82700 -75.54397 CLA8 TOP43 QUO143 36 ETH4 BOC6 RES23
2007 MI Michigan Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.5 0.3 1.1 12 White, non-Hispanic Maternal Race/Ethnicity 44.66132 -84.71439 CLA8 TOP43 QUO143 26 ETH4 BOC6 RES23
2007 NE Nebraska Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.4 0.1 2.3 1 Hispanic Maternal Race/Ethnicity 41.64104 -99.36572 CLA8 TOP43 QUO143 31 ETH2 BOC6 RES23
2007 CO Colorado Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.6 0.3 1.1 24 White, non-Hispanic Maternal Race/Ethnicity 38.84384 -106.13361 CLA8 TOP43 QUO143 8 ETH4 BOC6 RES23
2007 YC New York City Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.6 0.2 1.7 8 White, non-Hispanic Maternal Race/Ethnicity 42.82700 -75.54397 CLA8 TOP43 QUO143 36 ETH4 BOC6 RES23
2007 NE Nebraska Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.2 0.0 1.1 1 White, non-Hispanic Maternal Race/Ethnicity 41.64104 -99.36572 CLA8 TOP43 QUO143 31 ETH4 BOC6 RES23
2007 WY Wyoming Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.4 0.2 0.7 7 White, non-Hispanic Maternal Race/Ethnicity 43.23554 -108.10983 CLA8 TOP43 QUO143 56 ETH4 BOC6 RES23
2007 DE Delaware Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.2 0.1 0.7 2 White, non-Hispanic Maternal Race/Ethnicity 39.00883 -75.57774 CLA8 TOP43 QUO143 10 ETH4 BOC6 RES23
2007 WI Wisconsin Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.4 0.1 1.8 2 White, non-Hispanic Maternal Race/Ethnicity 44.39319 -89.81637 CLA8 TOP43 QUO143 55 ETH4 BOC6 RES23
2007 CO Colorado Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.4 0.2 0.9 11 Hispanic Maternal Race/Ethnicity 38.84384 -106.13361 CLA8 TOP43 QUO143 8 ETH2 BOC6 RES23
2007 GA Georgia Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.3 0.1 0.7 5 White, non-Hispanic Maternal Race/Ethnicity 32.83968 -83.62758 CLA8 TOP43 QUO143 13 ETH4 BOC6 RES23
2007 AK Alaska Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.6 0.2 1.6 12 White, non-Hispanic Maternal Race/Ethnicity 64.84508 -147.72206 CLA8 TOP43 QUO143 2 ETH4 BOC6 RES23
2007 RI Rhode Island Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.5 0.3 1.1 18 White, non-Hispanic Maternal Race/Ethnicity 41.70828 -71.52247 CLA8 TOP43 QUO143 44 ETH4 BOC6 RES23
2007 NC North Carolina Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.8 0.3 1.7 20 White, non-Hispanic Maternal Race/Ethnicity 35.46622 -79.15925 CLA8 TOP43 QUO143 37 ETH4 BOC6 RES23
2007 MN Minnesota Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.3 0.1 1.0 4 White, non-Hispanic Maternal Race/Ethnicity 46.35565 -94.79420 CLA8 TOP43 QUO143 27 ETH4 BOC6 RES23
2007 SC South Carolina Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.2 0.1 0.3 6 Hispanic Maternal Race/Ethnicity 33.99882 -81.04537 CLA8 TOP43 QUO143 45 ETH2 BOC6 RES23
2007 SC South Carolina Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.7 0.3 2.0 46 White, non-Hispanic Maternal Race/Ethnicity 33.99882 -81.04537 CLA8 TOP43 QUO143 45 ETH4 BOC6 RES23
2007 OH Ohio Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 5.7 0.8 31.2 1 Hispanic Maternal Race/Ethnicity 40.06021 -82.40426 CLA8 TOP43 QUO143 39 ETH2 BOC6 RES23
2007 WV West Virginia Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 1.0 0.5 1.9 31 White, non-Hispanic Maternal Race/Ethnicity 38.66551 -80.71264 CLA8 TOP43 QUO143 54 ETH4 BOC6 RES23
2007 OK Oklahoma Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.3 0.2 0.5 13 Hispanic Maternal Race/Ethnicity 35.47203 -97.52107 CLA8 TOP43 QUO143 40 ETH2 BOC6 RES23
2007 HI Hawaii Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.7 0.2 2.7 2 Hispanic Maternal Race/Ethnicity 21.30485 -157.85775 CLA8 TOP43 QUO143 15 ETH2 BOC6 RES23
2007 VT Vermont Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.6 0.3 1.2 11 White, non-Hispanic Maternal Race/Ethnicity 43.62538 -72.51764 CLA8 TOP43 QUO143 50 ETH4 BOC6 RES23
2007 OK Oklahoma Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.4 0.3 0.5 73 White, non-Hispanic Maternal Race/Ethnicity 35.47203 -97.52107 CLA8 TOP43 QUO143 40 ETH4 BOC6 RES23
2007 NJ New Jersey Infant Health Pregnancy Outcome Indicator of infant currently alive PRAMS NO 0.9 0.3 2.7 3 Hispanic Maternal Race/Ethnicity 40.13057 -74.27369 CLA8 TOP43 QUO143 34 ETH2 BOC6 RES23 
view(filtered_mortality_race)

This table shows in depth information about infant mortality rate by race-ethnicity in the 31 states in this sample. The race/ethnicity variable was re-categorized into just “Hispanic” and “White-non Hispanic” to give us our variables of interest. Using this information, we will visualize the relationship between infant mortality rate and how race/ethnicity plays a role.

Plot showing the effect of Race/Ethnicity on Infant Mortality

plot_infant_deaths <- ggplot(filtered_mortality_race, aes(x = break_out, fill = break_out)) +
  geom_bar() +
  labs(title = "Infant Deaths by Ethnicity",
       x = "Ethnicity",
       y = "Total Infant Deaths") +
  scale_fill_manual(values = c("Hispanic" = "blue", "Non-hispanic" = "green", "White, non-Hispanic" = "pink")) +
  theme_minimal()

print(plot_infant_deaths)

The plot_infant_deaths above shows a plot of infant deaths categorized by whether they were Hispanic or not. The graph shows that those who were not Hispanic had a higher infant death count than those who were Hispanic.

Plot of Infant Mortality and Maternal Income

filtered_mortality_income <- cleaned_infant_mortality %>%
  filter(break_out_category == "Income (years 2004 and beyond)" &
         (break_out %in% c("Less than $10,000", "$10,000 to $24,999", "$25,000 to $49,999", "$50,000 or more")) &
         question == "Indicator of infant currently alive" & response == "NO")

view(filtered_mortality_income)

plot_infant_income <- ggplot(filtered_mortality_income, aes(x = break_out, fill = break_out)) +
  geom_bar() +
  labs(title = "Infant Deaths by Income",
       x = "Maternal Income",
       y = "Total Infant Deaths") +
  scale_fill_manual(values = c("Less than $10,000" = "blue", "$10,000 to $24,999" = "red", "$25,000 to $49,999" = "purple", "$50,000 or more" = "pink")) +
  theme_minimal()

print(plot_infant_income)

This plot shows the relationship between maternal income levels and infant mortality rate. From the bar graph, those in the income bracket of $25,000-$49,000 had a higher infant mortality rate and those who made more than $50,000 had the lowest. This was an interesting observation, as those who made less that $10,000 a year would be expected to have the highest infant mortality as low income levels is a social determinant of infant health outcomes.

Plot of Infant Mortality and Maternal Age

filtered_mortality_age <- cleaned_infant_mortality %>%
  filter(break_out_category == "Maternal Age - 18 to 44 years in groupings" &
         (break_out %in% c("Age < 18", "Age 18 - 24", "Age 25 - 29", "Age 30 - 44", "Age 45+")) &
         question == "Indicator of infant currently alive" & response == "NO")

plot_mortality_age <- ggplot(filtered_mortality_age, aes(x = break_out, fill = break_out)) +
  geom_bar() +
  labs(title = "Infant Deaths by Maternal Age",
       x = "Maternal Age",
       y = "Total Infant Deaths") +
  scale_fill_manual(values = c("Age < 18" = "blue", "Age 18 - 24" = "purple", "Age 25 - 29" = "pink", "Age 30 - 44" = "yellow", "Age 45+" = "orange")) +
  theme_minimal()

print(plot_mortality_age)

This plot shows infant mortality rates by age. Interestingly, maternal populations below the age of 18 have the lowest infant mortality rates. Comparatively, women in the 45+ age category have a higher infant mortality rate. Women in the 18-24, 25-29 and 30-44 all have approximately the same levels of infant mortality rates.

Plot of Maternal Education and Infant Death

filtered_mortality_educ <- cleaned_infant_mortality %>%
  filter(break_out_category == "Maternal Education" &
         (break_out %in% c("<12 yrs", "12 yrs", ">12 yrs")) &
         question == "Indicator of infant currently alive" & response == "NO")

plot_mortality_educ <- filtered_mortality_educ |>
ggplot(aes(x = break_out, fill = break_out)) +
  geom_bar() +
  labs(title = "Infant Deaths by Maternal Education",
       x = "Maternal Education",
       y = "Total Infant Deaths") +
  scale_fill_manual(values = c("<12 yrs" = "blue", "12 yrs" = "purple", ">12 yrs" = "pink")) +
  theme_minimal()

print(plot_mortality_educ)

This plot shows the relationship between maternal education and infant mortality rates. This is an interesting finding that shows those with greater than 12 years of education had higher infant mortality rates compared to those with less than 12 years of education. This could be because factors like race could have been a confounding variable. Additionally, it can also be assumed that people who were in school longer were also older, so that could also be an additional factor.

Plot of Maternal Medicaid Recepient and Infant Death

filtered_mortality_medi <- cleaned_infant_mortality %>%
  filter(break_out_category == "Medicaid Recipient" &
         (break_out %in% c("Non-Medicaid", "Medicaid")) &
         question == "Indicator of infant currently alive" & response == "NO")

plot_mortality_medi <- filtered_mortality_medi |>
ggplot(aes(x = break_out, fill = break_out)) +
  geom_bar() +
  labs(title = "Infant Deaths by Medicaid Recpient",
       x = "Medicaid Recepient",
       y = "Total Infant Deaths") +
  scale_fill_manual(values = c("Non-Medicaid" = "blue", "Medicaid" = "purple")) +
  theme_minimal()

print(plot_mortality_medi)

This plot shows the relationship between infant mortality rate and mothers who were recipients of medicaid. This bar graph shows that there is no statistical difference.

Plot of Maternal Intent and Infant Death

filtered_mortality_intent <- cleaned_infant_mortality %>%
  filter(break_out_category == "Pregnancy Intendedness" &
         (break_out %in% c("Unintended", "Intended")) &
         question == "Indicator of infant currently alive" & response == "NO")

plot_mortality_intent <- filtered_mortality_intent |>
ggplot(aes(x = break_out, fill = break_out)) +
  geom_bar() +
  labs(title = "Infant Deaths by Maternal Intent",
       x = "Maternal Intent",
       y = "Total Infant Deaths") +
  scale_fill_manual(values = c("Unintended" = "orange", "Intended" = "red")) +
  theme_minimal()

print(plot_mortality_intent)

This plot shows the relationship between maternal intent to have children with infant mortality rate. Those who intended to have a child, actually had a higher rate of infant mortality rate. There could be several confounding factors that could contribute to this, such as age and stress factors.